Moonshot's newly released Kimi K3 is a 2.8 trillion parameter model that marks their most capable release to date. This article explores its performance benchmarks, pricing structures compared to Anthropic’s Sonnet series, and how it handles complex tasks like SVG generation. Through the lens of the pelican benchmark, the author examines the relationship between reasoning token consumption, cost, and a model's spatial awareness.
Main points:
- K3 is described as an open 3T-class model with high performance on long-horizon knowledge work.
- The pricing structure for K3 represents a significant increase over previous Moonshot models.
- Testing the pelican prompt reveals high reasoning token usage and substantial costs per task.
- While useful for checking spatial awareness, simple benchmarks fail to test agentic tool calling.
Main points:
- K3 is described as an open 3T-class model with high performance on long-horizon knowledge work.
- The pricing structure for K3 represents a significant increase over previous Moonshot models.
- Testing the pelican prompt reveals high reasoning token usage and substantial costs per task.
- While useful for checking spatial awareness, simple benchmarks fail to test agentic tool calling.
OpenAI has released the GPT-5.6 model family, comprising three sizes: Luna (smallest), Terra, and Sol (largest). These models feature a one million token context window, 128,000 maximum output tokens, and a knowledge cutoff of February 16th, 2026. Key updates to the API include programmatic tool calling via JavaScript orchestration, native multi-agent support for parallel task execution, explicit prompt cache breakpoints, and an option to receive unresized images in requests.
* Three model tiers: Luna, Terra, and Sol
* Improved performance in long-running agentic professional workflows
* New API features including programmatic tool calling and multi-agent orchestration
* Three model tiers: Luna, Terra, and Sol
* Improved performance in long-running agentic professional workflows
* New API features including programmatic tool calling and multi-agent orchestration
Simon Willison reviews Ornith-1.0, a new series of open-weights models from DeepReinforce designed for agentic coding. Built on Gemma 4 and Qwen 3.5, these models range from 9B to 397B parameters and demonstrate high proficiency in coding benchmarks and tool-use tasks.
- Available in 9B Dense, 31B Dense, 35B MoE, and 397B MoE variants
- Capable of navigating codebases and executing complex tool calls
- Built on Apache 2.0 licensed base models
- Available in 9B Dense, 31B Dense, 35B MoE, and 397B MoE variants
- Capable of navigating codebases and executing complex tool calls
- Built on Apache 2.0 licensed base models
Simon Willison explores his latest approach to running untrusted Python code safely within applications by utilizing MicroPython inside a WebAssembly (WASM) sandbox. The project addresses the security risks of plugin systems where code normally executes with full privileges, potentially leading to data leaks or system compromise. By leveraging wasmtime and an alpha package called micropython-wasm, Willison demonstrates how to enforce memory and CPU limits while providing controlled access to host functions through a custom thread-based request queue for persistent interpreter state.
Main topics:
- Security challenges in Python plugin systems
- Advantages of WebAssembly as a sandboxing technology
- Building the micropython-wasm alpha package
- Implementation details for persistent state and host functions
- Integration with Datasette Agent to execute code via LLMs
Main topics:
- Security challenges in Python plugin systems
- Advantages of WebAssembly as a sandboxing technology
- Building the micropython-wasm alpha package
- Implementation details for persistent state and host functions
- Integration with Datasette Agent to execute code via LLMs
* **Rapid Model Competition:** The title of "best model" shifted frequently between Anthropic (Claude), OpenAI (GPT), and Google (Gemini) during November 2025.
* **Advancements in Coding Agents:** Using Reinforcement Learning from Verifiable Rewards, coding agents transitioned from being unreliable to becoming dependable "daily-driver" tools for professional work.
* **Rise of Personal AI Assistants ("Claws"):** The emergence of highly popular local personal assistant projects like OpenClaw (formerly Warelay), leading to increased demand for hardware like Mac Minis to run them locally.
* **Gemini 3.1 Pro Release:** Google released an updated model with improved capabilities in visual/SVG generation.
* **Google Gemma 4 Series:** The release of highly capable open-weight models from a US company.
* **GLM-5.1 Release:** A massive, 754B parameter (1.5TB) open-weight model released by the Chinese lab GLM.
* **High-Performance Local Models:** Small, laptop-runnable open-weight models like Qwen3.6-35B-A3B began wildly outperforming expectations and competing with much larger frontier models in specific tasks.
* **Advancements in Coding Agents:** Using Reinforcement Learning from Verifiable Rewards, coding agents transitioned from being unreliable to becoming dependable "daily-driver" tools for professional work.
* **Rise of Personal AI Assistants ("Claws"):** The emergence of highly popular local personal assistant projects like OpenClaw (formerly Warelay), leading to increased demand for hardware like Mac Minis to run them locally.
* **Gemini 3.1 Pro Release:** Google released an updated model with improved capabilities in visual/SVG generation.
* **Google Gemma 4 Series:** The release of highly capable open-weight models from a US company.
* **GLM-5.1 Release:** A massive, 754B parameter (1.5TB) open-weight model released by the Chinese lab GLM.
* **High-Performance Local Models:** Small, laptop-runnable open-weight models like Qwen3.6-35B-A3B began wildly outperforming expectations and competing with much larger frontier models in specific tasks.
Explores how to use the llm CLI tool within a script's shebang line by utilizing the env -S pattern. This approach allows plain text files to become executable via large language models, treating file contents as prompts. The article covers using flags like -f for fragments and -x for code extraction, integrating tools such as llm_time, and leveraging YAML templates with parameters or embedded Python tool functions to create highly functional scripts.
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
Simon Willison discusses why requesting HTML rather than Markdown as an LLM output format can significantly enhance technical explanations. While token constraints previously favored Markdown, modern models benefit from the ability of HTML to incorporate SVG diagrams, interactive widgets, and improved navigation. The article provides prompt examples for reviewing pull requests via HTML artifacts and showcases a GPT-5.5 generated explanation of a Linux security exploit that uses CSS and JavaScript to create a rich documentation experience.
An exploration of the new Qwen3.6-27B open weight model, which claims flagship-level agentic coding performance that surpasses previous larger MoE models while being significantly smaller in size. The author tests a quantized version using llama-server and demonstrates its impressive ability to generate complex SVG graphics locally.
Key points:
- Qwen3.6-27B outperforms the older Qwen3.5-397B-A17B on coding benchmarks.
- Dramatic reduction in model size from 807GB to approximately 55.6GB for the base version.
- Successful local execution using a 16.8GB quantized GGUF version via llama.cpp.
- High-quality SVG generation capabilities for complex prompts like a pelican riding a bicycle.
Key points:
- Qwen3.6-27B outperforms the older Qwen3.5-397B-A17B on coding benchmarks.
- Dramatic reduction in model size from 807GB to approximately 55.6GB for the base version.
- Successful local execution using a 16.8GB quantized GGUF version via llama.cpp.
- High-quality SVG generation capabilities for complex prompts like a pelican riding a bicycle.
Simon Willison tests OpenAI's newly released ChatGPT Images 2.0 model using a complex Where's Waldo style prompt involving a raccoon holding a ham radio. By comparing results against previous versions and competitors like Google's Nano Banana, the article evaluates the model's ability to handle high-detail illustrations and specific text elements.
Django co-creator Simon Willison predicts the emergence of a "dark factory" era in software development, where AI-driven automation handles the entire coding process without human intervention. Drawing an analogy from automated manufacturing, Willison suggests that if machines can operate reliably without human oversight, there is no need for "lights on" monitoring. As AI tools increasingly take over complex programming tasks, the role of human developers may shift from writing code to merely providing creative direction. This evolution raises significant questions about the future of the global workforce and the potential for widespread displacement in the tech industry.
